Research Engineer, Bioresilience
Confirmed on the employer's own hiring board on Oct 30, 2026. First seen by Alion on Oct 28, 2026.
About the job
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
As a part of the Research Engineer to join the Bioresilience Initiative (https://deepmind.google/blog/our-approach-to-bioresilience/) within the DeepMind Impact Accelerator team. In this role, you will build and deploy novel applications leveraging Google’s frontier AI and generative models.
As a part of the team, you will accelerate DeepMind’s societal impact on critical global challenges, collaborate with Biosecurity, GenAI, and Alphabet partners like Google Research, Cloud, and Isomorphic Labs, will develop solutions across biological threat prevention, early pathogen detection, and rapid response. You will translate cutting-edge AI breakthroughs into scalable tools that protect communities worldwide.
Your position requires domain expertise in life sciences, computational biology, or biosecurity alongside solid software engineering skills to prototype, benchmark, and scale products using state-of-the-art GenAI tools.
Join DeepMind to advance AI development, protect society from biological threats, and accelerate high-quality product innovation for public benefit.Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer varied learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
- Partner with product leadership, external authorities (biosecurity organizations, institutes, public health bodies), and cross-functional teams to navigate ambiguity, understand real-world biodefense challenges, and define technical architectures across Bioresilience workstreams.
- Curate specialized biological datasets, design rigorous domain evaluations, and execute fine-tuning to maximize model performance on complex biological tasks.
- Engineer 0-to-1 solutions and workflows using internal platforms, building sophisticated agents powered by foundation models such as Gemini, AlphaFold, and AlphaGenome.
- Synthesize partner insights, edge cases, and deployment signals to inform future frontier model development and biological safety guardrails.
- Advocate safe GenAI deployment best practices in bioresilience, demonstrating peak frontier model capabilities to solve critical biological problems end-to-end.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications:
- Experience applying deep learning models to biological data (such as functional genomics or protein sequence data).
- Experience manipulating common life science data types including FASTQ, SAM/BAM, gff, BED, VCF, and PDB/CI using bioinformatics or health informatics tools.
- Demonstrated domain knowledge in life sciences, computational biology, or biosecurity (e.g., molecular biology, pathogen genomics, computational plant pathology, protein engineering, bioinformatics, or metagenomics).
- Ability to identify, break down, and solve ambiguous and complex process and technical problems that span across many teams and organizations.
- Demonstrated ability to translate biological concepts into computational workflows and evaluation benchmarks.